FAQ: Introduction to NumPy - Review


This community-built FAQ covers the “Review” exercise from the lesson “Introduction to NumPy”.

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This exercise can be found in the following Codecademy content:

Data Science

Introduction to Statistics with NumPy

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There is not a lot of discussion on some of the tasks asked to be performed in here, specifically 6 and 7 which could be considered difficult to a newer python learner.

6. “Hmmm, interesting” the researcher mumbles, “can you also give me the 6:00 AM data for Thursday and Friday?” Save this data to a new array thursday_friday_morning .

If you look at the two-dimensional array of:

[[ 46.6  48.1  61.8  56. ]
 [ 50.   47.5  61.3  55.6]
 [ 49.7  47.2  60.9  55.2]
 [ 49.5  47.1  60.6  54.9]
 [ 49.2  46.9  60.2  54.5]]

Then start the process by printing out something like this where we slice a start of 3, with a not inclusive stop of 5


You will get, something like below, which

[[ 49.5 47.1 60.6 54.9] [ 49.2 46.9 60.2 54.5]]

Good start, but not what the lesson wants, it only wants the 6am temperature for Thursday/Friday, not all the temperatures. You will use ‘,’ in the slice to select the element of the second dimension, 6am is element 1.

thursday_friday_morning = temperatures_fixed[3:5,1]


thursday_friday_morning = np.array(temperatures_fixed[3:5,1])

both will work

7. Select all temperatures that are under 50 degrees or over 60 degrees and save them to a new array temperature_extremes .

Past in some looping related tutorials they have shown that you can create a list from another list with some conditions using:

var = [l for l in list if <some condition with l>]

You can also perform a nested loop by doing:

var = [l for l in list for ll in l if <condition>]

So with that understanding you can do the following:

temperature_extremes = [float(t) for tf in temperatures_fixed for t in tf if t < 50 or t > 60]

Which will give you the output and the correct answer:

[46.6, 48.1, 61.8, 47.5, 61.3, 49.7, 47.2, 60.9, 49.5, 47.1, 60.6, 49.2, 46.9, 60.2]